Solar photovoltaic panel and roofing material detection using WorldView-3 imagery

Abstract

This PhD dissertation focuses on the development of new techniques to detect urban solar photovoltaic (PV) panel installations and roofing materials utilizing the commercially available WorldView-3 satellite imagery, consisting of 1 panchromatic (Pan) band with 0.3m resolution, 8 visible and near infrared (VNIR) bands with 1.2m resolution, and 8 short wave infrared (SWIR) spectral bands with 7.5m resolution. To accurately detect urban solar PV panels and roofing materials, it is necessary to analyze the spectral information in both the 8 VNIR bands and the 8 SWIR bands at the pixel level. However, the resolution difference between the VNIR bands and the SWIR bands is more than 6 times, which creates significant challenges for the spectral analysis and thus for the material detection. In order to increase the resolution of the SWIR bands from 7.5m to 1.2m, Fuze Go SWIR Sharp (FGSS) algorithm is used. The resulting high-resolution 1.2m SWIR bands are then combined with the original 1.2m VNIR bands to form a 16-band 1.2m (VNIR+SWIR) super spectral imagery. A method to detect solar PV panel installations and a method to detect roofing materials in the 16-band super spectral imagery are also developed. In order to increase the resolution of WorldView-3 SWIR bands from 7.5m to 1.2m and take advantage of their capability for material identification, this research investigated the capacities of 9 popular, industry adopted pan-sharpening algorithms for pan-sharpening the WorldView-3 SWIR bands. The general principles of the pan-sharpening algorithms are reviewed. The WorldView-3 Pan images were down-sampled from 0.4m to 1.6m to fuse with the 7.5m SWIR image. Experiments demonstrate that the most commonly used algorithms are not suitable for pan-sharpening SWIR images, whereas the new pan-sharpening algorithm, Fuze Go SWIR Sharp (FGSS), can produce satisfactory results. The reasons why most algorithms fail to produce quality pan-sharpened SWIR bands are also examined. To detect solar PV panels, a new method is developed that can effectively analyze the spectral information in the newly formed high-resolution (HR) 16-band 1.2m super spectral (SS) imagery by adapting the spectral angle mapping (SAM) algorithm. The proposed method, named HR-SSF-SAM method, is tested on the WorldView-3 imagery of Brea, California, USA. The results demonstrate a true detection rate of 93.3% with 0% false detection. Even solar PV panels and glass roofs can be differentiated from each other. To detect roofing materials, such as fiberglass, ethylene propylene diene monomer (EPDM), metal, and concrete, using WorldView-3 imagery, a novel method is proposed. The method utilizes the newly formed high-resolution 16-band 1.2m super spectral imagery and introduces a new approach to detect roofing materials. Experiments with the WorldView-3 imagery of Brea, California, USA, demonstrate that the proposed method achieves an overall accuracy of 97.59% and Kappa accuracy of 95.59% for roofing material detection in commercial areas, and an overall accuracy of 93.88% and Kappa accuracy of 88.98% for roofing material detection in residential areas with family houses. Because of the complexity of using WorldView-3 imagery for solar PV panel detection and roofing material detection, very few publications can be found in this area. The literature review undertaken for this research confirms that the accuracies achieved are significantly better than those found in the literature.